Better supporting workers in ML workplaces

This workshop is aimed at bringing together a multidisciplinary group to discuss Machine Learning and its application in the workplace as a practical, everyday work matter. It's our hope this is a step toward helping us design better technology and user experiences to support the accomplishment of that work, while paying attention to workplace context. Despite advancement and investment in Machine Learning (ML) business applications, understanding workers in these work contexts have received little attention. As this category experiences dramatic growth, it's important to better understand the role that workers play, both individually and collaboratively, in a workplace where the output of prediction and machine learning is becoming pervasive. There is a closing window of opportunity to investigate this topic as it proceeds toward ubiquity. CSCW and HCI offer concepts, tools and methodologies to better understand and build for this future

Trust, identity, privacy, and security considerations for designing a peer data sharing platform between people living with HIV

Resulting from treatment advances, the Human Immunodeficiency Virus (HIV) is now a long-term condition, and digital solutions are being developed to support people living with HIV in self-management. Sharing their health data with their peers may support self-management, but the trust, identity, privacy and security (TIPS) considerations of people living with HIV remain underexplored. Working with a peer researcher who is expert in the lived experience of HIV, we interviewed 26 people living with HIV in the United Kingdom (UK) to investigate how to design a peer data sharing platform. We also conducted rating activities with participants to capture their attitudes towards sharing personal data. Our mixed methods study showed that participants were highly sophisticated in their understanding of trust and in their requirements for robust privacy and security. They indicated willingness to share digital identity attributes, including gender, age, medical history, health and well-being data, but not details that could reveal their personal identity. Participants called for TIPS measures to foster and to sustain responsible data sharing within their community. These findings can inform the development of trustworthy and secure digital platforms that enable people living with HIV to share data with their peers and provide insights for researchers who wish to facilitate data sharing in other communities with stigmatised health conditions